Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,651 detections
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Detects the execution of Python interpreters spawned by code editors or runtime environments (e.g., VS Code, Cursor, Node.js) with specific command-line arguments involving 'init'. This behavior may indicate an adversary attempting to leverage development tools to execute custom scripts, potentially for automated deployment, persistence, or malicious payload initialization.
Detects high-volume failed authentication attempts targeting a specific user account from a source IP address, indicative of 'MFA fatigue' or 'push bombing' attacks. This rule monitors endpoint-visible logon failure events as captured by EDR telemetry to identify potential attempts to circumvent multi-factor authentication by spamming the user with requests.
Detects instances where 'node.exe' or 'node' initiates the deletion of 'setup.cjs' from the 'node_modules/easy-day-js' directory. This includes both direct file system deletion (fs.rmSync) and shell-based deletion commands (e.g., 'del /f', 'rm -f') executed by 'node.exe'. This activity is indicative of potential malicious behavior, possibly related to supply chain attacks or post-exploitation cleanup.
Detects unauthorized processes attempting to access or perform operations on sensitive files such as browser credentials, SSH keys, crypto wallets, and password manager databases. The rule specifically looks for non-standard or unsigned processes interacting with a high volume of these files in a short time frame, which is indicative of credential harvesting by infostealer malware like Lumma, Redline, or Vidar.
Detects outbound network connections from workstations or servers to common message broker ports (MQTT 1883/8883, AMQP 5672). These protocols are sometimes abused for command-and-control (C2) communication, as they allow for pub/sub messaging patterns that can blend into legitimate network traffic.
Detects unauthorized access (creation, modification, renaming, or deletion) to sensitive local credential files by the Node.js runtime process (node.exe/node). This behavior is indicative of infostealers, specifically noted in the Miasma/MYRA activity associated with the Polymarket Trap campaign, where malicious packages attempt to exfiltrate AWS, SSH, NPM, Python, Docker, and GPG credentials.
This rule detects the installation and execution of malicious typosquatted npm packages associated with the Polymarket Trap campaign. It monitors for the creation of specific directory names within node_modules, npm install commands referencing these package names, and network activity or DNS lookups to 'log-taker' infrastructure from Node.js processes.
This rule detects the creation or modification of files that contain common certificate-related keywords such as 'crl', 'certificate', or 'pem'. This activity could indicate the deployment of new certificates, which might be legitimate or malicious, such as for establishing command and control, code signing, or evading detection.
This rule detects processes such as Node.js, npm, or Python accessing sensitive command-line history files including .bash_history, .zsh_history, .fish_history, or ConsoleHost_history.txt. Accessing these files, which often contain credentials or command execution history, is a common indicator of credential harvesting or post-exploitation reconnaissance.
Detects Node.js executing the malicious setup.cjs script with the --no-warnings flag. This execution sequence is associated with the easy-day-js typosquat postinstall hook used in the Mastra npm supply chain compromise.
This rule detects network activity associated with the StealC information stealer, specifically focusing on HTTP POST requests made to known command-and-control (C2) domains. It identifies characteristic URL patterns, such as randomly generated 16-character PHP files or specific index.php structures, which are used for bot registration and communication. The detection includes patterns indicative of RC4-encrypted payloads and behavior mimicking Amadey bot status checks.
Detects execution of the macOS 'security' utility to harvest generic or internet passwords from the Keychain, a technique commonly associated with the AMOS (Atomic macOS Stealer) infostealer for credential theft.
Detects the AMOS (Atomic macOS Stealer) infection chain where a shell (e.g., Terminal, bash) executes curl to download a DMG file to a temporary directory, followed immediately by the use of hdiutil with the -nobrowse flag to mount the disk image silently, effectively bypassing macOS Gatekeeper UI prompts to the user.
This rule detects a high frequency of successful network connections from a single device to multiple unique destination IP addresses within a one-hour window. The targeted ports (445, 139, 3389, 22, 21) are commonly used for remote administrative access, file transfers, and remote shell services, which are frequently leveraged by adversaries for lateral movement. The threshold of 3 unique targets in an hour may indicate automated scanning or credential brute-forcing activity across the network.
Detects a user mounting a deceptive .dmg file that invokes a hidden .sh or .command script upon opening. This rule specifically looks for the 'hdiutil' process attaching a disk image and then correlates it with a process executing a hidden shell script (.sh or .command) from a mounted volume.
Detects non-browser applications (Python, PowerShell, cURL, wget, node) communicating directly with OpenAI or ChatGPT API endpoints, indicative of automated AI tradecraft by threat actors. This rule specifically looks for network connections to api.openai.com initiated by these common scripting or command-line tools.
Detects network activity associated with the DcRAT trojan as identified in Operation DragonReturn. This includes outbound network connections to hardcoded C2 port 2671, connections to known malicious IP addresses, and DNS resolutions of identified attacker-controlled C2 domains.
Detects anomalous network traffic patterns communicating with the Telegram Bot API (api.telegram.org) from non-official Telegram processes. The rule identifies potential command and control (C2) activity by analyzing request frequency, specific API method usage (e.g., getUpdates, sendMessage), and beaconing-like behavior.
Detects Chrome launch with specific crash recovery evasion flags indicative of FlutterShell.
Detects activity associated with a specific phishing campaign distributing 'Distribution Settlement Agreement' PDF lures. The rule monitors for file creation or execution of files matching this naming pattern, as well as network connection attempts to domains identified as related to the threat actors: 'gasparinutrition.com' and 'gappssmtp.com'.
Detects network requests to Google image search containing an 'imgurl' parameter, which can be used by an attacker to exfiltrate data by forcing an external service to crawl a controlled URL containing sensitive information.
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